Field of the Invention
The present invention refers to communication systems. More particularly, the present invention relates to the field of wireless or mobile telecommunication networks. Even more particularly, the present invention relates to a method and a system adapted to determine the typology and/or the position of a plurality of relevant locations visited by individuals within a surveyed geographic area (e.g., a city, a municipality, etc.) based on information retrieved from a wireless or mobile telecommunication network(s) serving such surveyed area.
Overview of the Related Art
The capability of identifying places visited by individuals—referred to as relevant locations in the following—, along with a frequency with which such relevant locations are visited and typical time intervals in which the relevant locations are visited results to be extremely useful for a variety of analysis of human habits. For example, such information might be used for traffic analysis, which is fundamental for mobility planning such as management of public transport and/or for forecasting trends in traffic of vehicles. Other examples in which the above-mentioned information may be successfully used are the field of telecommunication network management and/or epidemiology (e.g., for forecasting epidemic diffusions).
Nowadays, mobile phones have reached a thorough diffusion among the population of many countries, and mobile phone owners almost always carry their mobile phones with them. Since mobile phones communicates with a plurality of base stations of the mobile phone networks, and each base station covers (i.e., serves) one or more predetermined geographic areas (or cells) which are known to the mobile phone network operator, mobile phones result to be optimal candidates as tracking devices for collecting data useful for identifying relevant locations visited by an individual, for how long the individual remains in each relevant location and how frequently each relevant location is visited.
In the art many systems and methods have been proposed in order to collect information about time and locations at and in which a User Equipment (UE, e.g. a mobile phone, a smartphone, a tablet, etc.) of an individual connects to the mobile phone network (e.g., for performing a voice call or sending a text message), and use such collected information in order to derive information related to relevant locations visited by the individual.
For example, N. Caceres, J. Wideberg, and F. Benitez “Deriving origin destination data from a mobile phone network”, Intelligent Transport Systems, JET, Vol. 1, No. 1, pages 15-26, 2007, describes a mobility analysis simulation of moving vehicles along a highway covered by a plurality of GSM network cells. In the simulation the entries of Origin-Destination matrices are determined by identifying the GSM cells used by the mobile phones in the moving vehicles for establishing voice calls or sending SMS.
M. C. González et al. “Understanding individual human mobility patterns”, Nature pages 453, 779-782, 2008 defined a probability density function adapted to identify home and work places by studying trajectories of 100,000 individuals tracked over a time interval of six months.
In US 2009/0157496 a personal broadcasting system and method is described for automatically matching and introducing users based on their real-time locations, such that users identified as capable of or likely to form a relationship can be notified of that fact at a point in time when it is easy to meet. A system and method is also described that allows at least one of two users that have visited or passed through the same location at different points in time to automatically receive information about the other despite the temporal separation between them.
In F. Calabrese et al. “Estimating Origin-Destination Flows Using Mobile Phone Location Data”, IEEE Pervasive, pages 36-44, October-December 2011 (Vol. 10 No. 4), a method is proposed that identifies a home location of an individual on the basis of a number of connections to a telecommunication network performed during night hours by a user equipment of the individual, and that identifies a work location of the individual according to a number of connections to a telecommunication network performed during business hours.
In R. A. Becker et al., “A Tale of One City: Using Cellular Network Data for Urban Planning,” IEEE Pervasive Computing, Vol. 10, No. 4, pages 18-26, 2011, a method is proposed for identifying a city in which an individual works by analyzing phone calls performed by the individual during business hours over a plurality of weekdays.
US 2006/0293046 proposes a method for exploiting data from a wireless telephony network to support traffic analysis. Data related to wireless network users are extracted from the wireless network to determine the location of a mobile station. Additional location records for the mobile station can be used to characterize the movement of the mobile station: its speed, its route, its point of origin and destination, and its primary and secondary transportation analysis zones. Aggregating data associated with multiple mobile stations allows characterizing and predicting traffic parameters, including traffic speeds and volumes along routes.
In S. Isaacman et al., “Identifying Important Places in People's Lives from Cellular Network Data”, in Pervasive Computing, Vol. 6696, pages 133-151, 2011 a method is proposed for recognizing locations visited by an individual through a clustering of base stations of a telecommunication network close to each other to which a User Equipment of the individual connects in order to perform communications. Moreover, the method identifies a home location and a work location of the individual by applying a logistic regression having coefficients determined through information provided by a control group of individuals.